Model-based Geostatistics for Global Public Health: Methods and Applications
Model-based Geostatistics for Global Public Health: Methods and Applications provides an introductory account of model-based geostatistics, its implementation in open-source software and its application in public health research. In the public health problems that are the focus of this book, the authors describe and explain the pattern of spatial variation in a health outcome or exposure measurement of interest. Model-based geostatistics uses explicit probability models and established principles of statistical inference to address questions of this kind.
Presents state-of-the-art methods in model-based geostatistics.
Discusses the application these methods some of the most challenging global public health problems including disease mapping, exposure mapping and environmental epidemiology.
Describes exploratory methods for analysing geostatistical data, including: diagnostic checking of residuals standard linear and generalized linear models; variogram analysis; Gaussian process models and geostatistical design issues.
Includes a range of more complex geostatistical problems where research is ongoing.
All of the results in the book are reproducible using publicly available R code and data-sets, as well as a dedicated R package.